School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2023 Jul 7;23(13):6219. doi: 10.3390/s23136219.
The landing gear structure suffers from large loads during aircraft takeoff and landing, and an accurate prediction of landing gear performance is beneficial to ensure flight safety. Nevertheless, the landing gear performance prediction method based on machine learning has a strong reliance on the dataset, in which the feature dimension and data distribution will have a great impact on the prediction accuracy. To address these issues, a novel MCA-MLPSA is developed. First, an MCA (multiple correlation analysis) method is proposed to select key features. Second, a heterogeneous multilearner integration framework is proposed, which makes use of different base learners. Third, an MLPSA (multilayer perceptron with self-attention) model is proposed to adaptively capture the data distribution and adjust the weights of each base learner. Finally, the excellent prediction performance of the proposed MCA-MLPSA is validated by a series of experiments on the landing gear data.
起落架结构在飞机起飞和着陆过程中承受着巨大的载荷，准确预测起落架性能有助于确保飞行安全。然而，基于机器学习的起落架性能预测方法对数据集的依赖性很强，其中特征维度和数据分布会对预测精度产生很大影响。针对这些问题，提出了一种新的 MCA-MLPSA 方法。首先，提出了一种 MCA（多元相关分析）方法来选择关键特征。其次，提出了一种异构多学习者集成框架，利用不同的基础学习者。第三，提出了一种 MLPSA（具有自注意力的多层感知机）模型，自适应地捕获数据分布并调整每个基础学习者的权重。最后，通过在起落架数据上进行的一系列实验验证了所提出的 MCA-MLPSA 的优异预测性能。
